Identifying Black Swans in NextGen: Predicting Human Performance in Off-Nominal Conditions

The failure of pilots to notice nonsalient unexpected events outside of foveal vision has led to many aircraft accidents. In this paper, a model of visual attention, N-SEEV (noticing— salience, expectancy, effort, and value), was developed to predict these failures. The model was validated against empirical data derived from a meta-analysis of pilots’ failure to notice safety-critical unexpected events. A total of 25 studies that reported objective data on miss rate for unexpected events in high-fidelity cockpit simulations were identified, and their miss rate data pooled across five variables (phase of flight, event expectancy, event location, presence of a head-up display, and presence of a highway-in-the-sky display). The parameters of the N-SEEV model then were tailored to mimic these dichotomies. Results showed that the N-SEEV model output successfully predicted variance in the obtained miss rate. The individual miss rates of all six dichotomous conditions were predicted within 14%, and four of these were predicted within 7%. These findings indicate that the N-SEEV model can be used to predict if new aviation technology and procedures may compromise safety in terms of pilots’ failing to notice unexpected events.

Language

  • English

Media Info

  • Media Type: Print
  • Features: Figures; References; Tables;
  • Pagination: pp 638-651
  • Serial:

Subject/Index Terms

Filing Info

  • Accession Number: 01150809
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Feb 11 2010 11:08PM